A Framework for Modeling Uncertainty
in Regional Climate Change
Erwan Monier, Xiang Gao, Jeffery Scott,
Andrei Sokolov and Adam Schlosser
Report No. 244
May 2013
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A Framework for Modeling Uncertainty in Regional Climate Change
Erwan Monier*† , Xiang Gao* , Jeffery Scott* , Andrei Sokolov* and Adam Schlosser*
Abstract
In this study, we present a new modeling framework and a large ensemble of climate projections to investigate the uncertainty in regional climate change over the US associated with four dimensions of uncertainty.
The sources of uncertainty considered in this framework are the emissions projections (using different climate policies), climate system parameters (represented by different values of climate sensitivity and net
aerosol forcing), natural variability (by perturbing initial conditions) and structural uncertainty (using different climate models). The modeling framework revolves around the Massachusetts Institute of Technology
(MIT) Integrated Global System Model (IGSM), an integrated assessment model with an intermediate complexity earth system model (with a two-dimensional zonal-mean atmosphere). Regional climate change over
the US is obtained through a two-pronged approach. First, we use the IGSM-CAM framework which links
the IGSM to the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM).
Secondly, we use a pattern-scaling method that extends the IGSM zonal mean based on climate change patterns from various climate models. Results show that uncertainty in temperature changes are mainly driven
by policy choices and the range of climate sensitivity considered. Meanwhile, the four sources of uncertainty contribute more equally to precipitation changes, with natural variability having a large impact in
the first part of the 21st century. Overall, the choice of policy is the largest driver of uncertainty in future
projections of climate change over the US.
Contents
1. INTRODUCTION ................................................................................................................................... 1
2. METHODOLOGY .................................................................................................................................. 2
2.1 Modeling Framework ..................................................................................................................... 2
2.2 Description of the Simulations ..................................................................................................... 4
3. RESULTS ................................................................................................................................................. 5
3.1 Time Series of US Mean Temperature and Precipitation ......................................................... 5
3.2 Regional Patterns of Change ......................................................................................................... 7
4. SUMMARY AND CONCLUSION ................................................................................................... 11
5. REFERENCES ...................................................................................................................................... 13
1. INTRODUCTION
It is well established that the uncertainty in climate system parameters and projected emissions
are important drivers of uncertainty in global climate change (Sokolov et al., 2009; Webster et al.,
2012). The climate system response to given emissions is essentially controlled by three climate
parameters: the climate sensitivity, the strength of aerosol forcing and the ocean heat uptake rate.
Future emissions are driven by future economic activity and technological pathways influenced
by climate policies and population growth. Other sources of uncertainty in future climate
projections, in particular at the regional level, include natural variability and structural uncertainty
associated with differences in parameterization in existing climate models. It is well known that
year-to-year variability in the climate system is large, in particular at high latitudes, making the
emergence of significant climate change slow and signal-to-noise detection difficult (Mahlstein
*
†
Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, MA.
Corresponding author (Email: emonier@mit.edu).
1
et al., 2011; Hawkins and Sutton, 2012; Mahlstein et al., 2012). At the same time, climate
projections are heavily influenced by the characteristics of the chosen climate model and global
climate models remain inconsistent in capturing regional precipitation changes and other
atmospheric processes. Quantifying uncertainty in future regional climate change would prove
beneficial to policymakers and impact modeling research groups who investigate climate change
and its societal impacts at the regional level, including agriculture productivity, water resources
and energy demand (Reilly et al., 2013).
In this study, we introduce a new modeling framework to investigate the uncertainty in
regional climate change over the US associated with four sources of uncertainty, namely: (i)
uncertainty in the emissions projections, using different climate policies; (ii) uncertainty in the
climate system parameters, represented by different values of climate sensitivity and net aerosol
forcing; (iii) natural variability, obtained by perturbing initial conditions; and (iv) structural
uncertainty using different climate models. The modeling framework is built around the
Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM) (Sokolov
et al., 2005, 2009), an integrated assessment model with an intermediate complexity earth system
model (with a two-dimensional zonal-mean atmosphere). Regional climate change over the US is
obtained through a two-pronged approach. First, we use the IGSM-CAM framework which links
the IGSM to the National Center for Atmospheric Research (NCAR) Community Atmosphere
Model (CAM) Monier et al. (2013). Secondly, we use a pattern-scaling method that extends the
IGSM zonal mean based on climate change patterns from various climate models (Schlosser
et al., 2007).
In this paper, we present a description of the framework for modeling uncertainty in regional
climate change. We give a description of the matrix of simulations and present results of regional
climate change over the US. We place a particular emphasis on quantifying the range of
uncertainty and identifying the contributions of different sources of uncertainty considered in this
study. The simulations presented here are part of a multi-model project to achieve consistent
evaluation of climate change impacts in the US (Waldhoff et al., 2013).
2. METHODOLOGY
2.1 Modeling Framework
In this study, the core simulations use the MIT IGSM version 2.3 (Dutkiewicz et al., 2005;
Sokolov et al., 2005, 2009), an integrated assessment model that couples an earth system model
of intermediate complexity to a human activity model. The earth system component of the IGSM
includes a two-dimensional zonally averaged statistical dynamical representation of the
atmosphere, a three-dimensional dynamical ocean component with a thermodynamic sea-ice
model and an ocean carbon cycle (Dutkiewicz et al., 2005, 2009) and a Global Land Systems
(GLS) that represents terrestrial water, energy and ecosystem processes (Schlosser et al., 2007),
including terrestrial carbon storage and the net flux of carbon dioxide, methane and nitrous oxide
from terrestrial ecosystems. The IGSM2.3 also includes an urban air chemistry model (Mayer
et al., 2000) and a detailed global scale zonal-mean chemistry model (Wang et al., 1998) that
2
considers the chemical fate of 33 species including greenhouse gases and aerosols. Finally, the
human systems component of the IGSM is the MIT Emissions Predictions and Policy Analysis
(EPPA) model (Paltsev et al., 2005), which provides projections of world economic development
and emissions over 16 global regions along with analysis of proposed emissions control measures.
Since the IGSM includes a human activity model, it is possible to analyze uncertainties in
emissions resulting from both uncertainty in model parameters and uncertainty in future climate
policy decisions. Another major feature is the flexibility to vary key climate parameters
controlling the climate response: climate sensitivity, net aerosol forcing and ocean heat uptake
rate. Because the IGSM has a two-dimensional zonal-mean atmosphere, it cannot be directly used
to simulate regional climate change. To simulate climate change over the US, we use a
two-pronged method.
On the one hand, the MIT IGSM-CAM framework (Monier et al., 2013) links the IGSM to the
National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM)
(Collins et al., 2006b), with new modules developed and implemented in CAM to allow climate
parameters to be changed to match those of the IGSM. In particular, the climate sensitivity of
CAM is changed using a cloud radiative adjustment method (Sokolov and Monier, 2012). In the
IGSM-CAM framework, CAM is driven by greenhouse gas concentrations and aerosol loading
computed by the IGSM model as well as IGSM sea surface temperature (SST) anomalies from a
control simulation corresponding to pre-industrial forcing superposed on an observed monthly
climatology from the merged Hadley-OI SST, a surface boundary dataset designed for uncoupled
simulations with CAM (Hurrell et al., 2008). More details on the IGSM-CAM framework can be
found in (Monier et al., 2013).
On the other hand, a pattern scaling method (Schlosser et al., 2012) extends the latitudinal
projections of the IGSM 2-D zonal-mean atmosphere by applying longitudinally resolved patterns
from observations, and from climate-model projections archived from exercises carried out for the
Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC)
(Meehl et al., 2007). The pattern scaling method relies on transformation coefficients that
essentially reflect the relative value of any given variable at a specific grid cell in relation to its
zonal mean. These transformation coefficients are calculated for each month of the year. The
following scheme is applied to expand IGSM zonal mean variables across the longitude:
!
AR4
dC
x,y
IGSM
OBS
· ∆TGlobal · VyIGSM
(1)
Vx,y
= Cx,y
+
dTGlobal
Obs/AR4
Obs/AR4
Cx,y
=
Vx,y
(2)
Obs/AR4
Vy
IGSM
where x and y are the longitude and latitude of a grid cell, VyIGSM and Vx,y
are the original
zonal mean IGSM data and the transformed IGSM data, ∆TGlobal is the global IGSM temperature
AR4
dCx,y
OBS
changes from present day. Cx,y
are the transformation coefficients for present day and dTGlobal
are the rates of change of the transformation coefficient from IPCC AR4 models with respect to
3
global temperature change. The pattern scaling method is applied to surface air temperature,
precipitation, surface wind speed, surface relative humidity, total cloud cover, sea surface
OBS
are calculated for present-day conditions
temperature and surface water vapor pressure. Cx,y
(for the period 1980–2009) using the Modern Era Retrospective-analysis for Research and
Applications (MERRA, Rienecker et al., 2011) for all variables except for precipitation, which
relies on the Global Precipitation Climatology Project (GPCP, Adler et al., 2003). GPCP is
preferred over MERRA for precipitation because of the presence of substantial biases and
discontinuity in the MERRA precipitation over the 1980–2009 period (Kennedy et al., 2011;
AR4
dCx,y
are calculated based on the difference in 10-year mean
Rienecker et al., 2011). Finally, dTGlobal
AR4
climatology of Cx,y between present-day conditions and the time of doubling of CO2 in the
IPCC simulations with a 1% per year increase in CO2 (equivalent to year 70 of the simulation),
divided by the global mean temperature difference of the same time period. More details on the
pattern scaling method can be found in (Schlosser et al., 2012).
The IGSM-CAM simulations provides daily output at a resolution of 2 ◦ x 2.5 ◦ while the
IGSM-pattern scaling method provides monthly output at the same 2 ◦ x 2.5 ◦ horizontal
resolution.
2.2 Description of the Simulations
To investigate the uncertainty in projections of future climate change, a core of 12 simulations
with the IGSM are conducted with four values of climate sensitivity and three emissions scenarios
are considered. The three emissions scenarios are (i) a reference scenario with unconstrained
emissions after 2012 (REF), with a total radiative forcing of 9.7 W/m2 by 2100; (ii) a stabilization
scenario (POL4.5), with a total radiative forcing of 4.5 W/m2 by 2100; and (iii) a more stringent
stabilization scenario (POL3.7), with a total radiative forcing of 3.7 W/m2 by 2100. The four
values of climate sensitivity (CS) considered are 2.0, 3.0, 4.5 and 6◦ C, which represent
respectively the lower bound (CS2.0), best estimate (CS3.0) and upper bound (CS4.5) of climate
sensitivity based on the Fourth Assessment Report of the Intergovernmental Panel on Climate
Change (IPCC, 2007), and a low probability/high risk climate sensitivity (CS6.0). The associated
net aerosol forcing was chosen to ensure a good agreement with the observed climate change over
the 20th century.
For each set of emissions scenario and climate sensitivity, the IGSM-CAM is run with five
different initial conditions (Monier et al., 2013) and the IGSM-pattern scaling is applied to four
different patterns of regional climate change. Three IPCC AR4 climate models are chosen along
with the IPCC AR4 multi-model ensemble mean. First, the NCAR Community Climate System
Model version 3 (CCSM3.0) (Collins et al., 2006a) is chosen to compare with the IGSM-CAM
results since both modeling systems have the same atmospheric components. However, because
they have different ocean components, simulations with the IGSM-CAM and the IGSM-pattern
scaling with CCSM3.0 are not necessarily expected to be identical. Nonetheless, this provides an
opportunity to examine if the relative simple pattern-scaling scheme is sufficiently effective to
replicate what can be represented in a more sophisticated three-dimensional climate model. The
4
two additional models chosen are the models with the largest and smallest projected increases in
precipitation over the US, respectively, the Bjerknes Centre for Climate Research Bergen Climate
Model version 2.0 (BBCR BCM2.0, Otterâ et al., 2009) and the Model for Interdisciplinary
Research on Climate version 3.2 medium resolution (MIROC3.2 medres, Hasumi and Emori,
2004). Finally, the multi-model ensemble pattern of regional climate change is obtained from the
17 IPCC AR4 climate models.
Overall, the modeling framework and experimental design used in this study investigates
uncertainty in regional climate change associated with four dimensions of uncertainty: emissions
projections, the global climate response, the natural variability, and structural uncertainty. A
summary of the simulation matrix is shown in Figure 1 .
3. RESULTS
In the remainder of this article, we refer to present day as the mean over the 1991–2010 period
and to 2100 as the mean over the 2091–2110 period.
3.1 Time Series of US Mean Temperature and Precipitation
Figure 2 shows time series of US mean surface air temperature and precipitation anomalies
from present day for all the simulations with the IGSM-CAM, their ensemble mean and the
IGSM-pattern scaling along with observations. The simulations in this study exhibit a wide range
of changes by the end of the century relative to present day. The projected warming ranges from
less than 1.0◦ C to about 10◦ C while the changes in precipitation range from a decrease of
-0.1 mm/day to increases up to 0.7 mm/day. The largest changes generally occur for the reference
INITIAL
CONDITIONS
CLIMATE
SENSITIVITY
EMISSIONS
SCENARIO
EMISSIONS
SCENARIO
CLIMATE
SENSITIVITY
MODEL
PATTERNS
CS2.0
MIROC2.3
medres
CS3.0
NCAR
CCSM3.0
CS4.5
BCCR
BCM2.0
CS6.0
Multi-model
mean
INIC1
CS2.0
INIC2
REF
REF
CS3.0
INIC3
POL4.5
IGSM-CAM
IGSM
IGSM
pattern scaling
POL4.5
CS4.5
INIC4
POL3.7
POL3.7
CS6.0
INIC5
60 IGSM-CAM SIMULATIONS
48 IGSM-PATTERN SCALING SIMULATIONS
TOTAL OF 108 SIMULATIONS
Figure 1. Summary of the experimental design of the modeling framework and the simulation matrix used
in this study.
5
Figure 2. Time series of US mean a) surface air temperature and b) precipitation anomalies from present
day (1991–2010 mean) for all the simulations with the IGSM-CAM, their ensemble mean and the
IGSM-pattern scaling along with observations. The black lines represent observations, the Goddard
Institute for Space Studies (GISS) surface temperature (GISTEMP, Hansen et al., 2010) and the 20th
Century Reanalysis V2 precipitation (Compo et al., 2011). The blue, green, orange and red lines
represent, respectively, the simulations with a climate sensitivity of 2.0, 3.0, 4.5 and 6.0◦ C. The solid,
dashed and dotted lines represent, respectively, the simulations with the reference scenario,
stabilization scenario at 4.5 W/m2 and the stabilization scenario at 3.7 W/m2 .
emissions scenarios and for the highest climate sensitivity. The IGSM-CAM simulations display
a strong interannual variability, especially for precipitation, which is in very good agreement with
the observations over the historical period. As a result, comparing two particular simulations to
6
identify the impact of, for example, the implementation of a stabilization policy or different
values of climate sensitivity, is generally difficult. However, once the five-member ensemble is
averaged, the signal is more easily extracted from the noise. Meanwhile, simulations with the
IGSM-pattern scaling method show limited interannual variability, even less than the
IGSM-CAM ensemble mean simulations. This is because the temporal variability in temperature
and precipitation in the IGSM-pattern scaling method is controlled entirely by the IGSM zonal
mean, which displays a much weaker variability than would any particular grid cell along the
same latitude. For this reason, the IGSM-pattern scaling method underestimates natural
variability and its potential changes, as well as climate and weather extreme events. Finally, the
choice of the model for the pattern scaling method has limited impact on the US mean surface air
temperature changes but a wide range of changes in projected precipitation changes. One model
in particular is showing decreases in the US mean precipitation, except for the CS4.5 REF and
CS6.0 REF after 2080. This reflects the large uncertainty in projections of precipitation, in
particular over the US, by the IPCC AR4 models (Randall et al., 2007) and the strength of the
IGSM-pattern scaling method in accounting for structural uncertainty.
3.2 Regional Patterns of Change
Figure 3 shows maps of changes in surface air temperature in 2100 relative to present day for
the IGSM-CAM and IGSM-pattern scaling simulations, for different initial conditions, different
values of climate sensitivity, different emissions scenarios and different models. Overall, Figure 3
displays a wide range of warming amongst the simulations presented in this study. The
IGSM-CAM simulations with different initial conditions under the same CS3.0 REF scenario
show very similar patterns of temperature change. The largest warming takes place over the Great
Basin while the West South Central States show the least amount of warming. Differences
between the five initial conditions are less than 1.0◦ C, largely due to the use of 20-year averages
in this analysis. The IGSM-CAM ensemble mean for the REF emissions scenario with different
values of climate sensitivity show a wide range in the magnitude of the warming. The largest
warming is seen for the ensemble mean with the highest climate sensitivity, where increases in
temperature over the Great Basin reach up to 12.0◦ C. On the other hand, the warming is reduced
to just 5◦ C in the ensemble mean with the lowest value of climate sensitivity. The differences in
warming due to the implementation of different emissions scenarios under the same climate
sensitivity (CS3.0) can also be seen in Figure 3. It shows that both stabilization policies greatly
reduce the warming, with increases in temperature less than 3.0◦ C over the entire US. Finally,
simulations with the IGSM-pattern scaling method for the same CS3.0 REF scenario show that
different models display different patterns of warming. In particular, the location of the largest
warming differs significantly between the models. Furthermore, the maximum warming amongst
the four pattern-scaling models and the IGSM-CAM for the same REF CS3.0 scenario ranges
from 6.0 to 8.0◦ C.
A similar analysis for changes in precipitation is shown in Figure 4. The IGSM-CAM
generally shows decreases in precipitation on the West Coast and increases everywhere else. The
7
Figure 3. Changes in surface air temperature (in ◦ C) in 2100 (2091–2110 mean) relative to present day
(1991–2010 mean) for a) the IGSM-CAM simulations with different initial conditions under the
REF CS3.0 scenario, b) the IGSM-CAM ensemble mean with different climate sensitivities under the
REF scenario, c) the IGSM-CAM ensemble mean with different emissions scenarios under the CS3.0
scenario and d) the IGSM-pattern scaling with different climate models under the REF CS3.0 scenario.
use of different initial conditions lead to distinctively different locations and magnitudes of
maximum changes in precipitation as well as a zero-change line in a slightly different location.
For example, the simulation with initial condition 5 displays the largest decrease in precipitation,
located on the coast of Oregon, reaching up to 1 mm/day compared to around 0.5 mm/day in the
other simulations. The magnitude of the precipitation decrease over the West Coast can be larger
between IGSM-CAM simulations with different initial conditions but under the same CS3.0 REF
scenario than between ensemble simulations with different values of climate sensitivity under the
same REF scenario. This underlines the fact that initial conditions have a larger impact on
regional precipitation changes than on temperature. The impact of the climate sensitivity appears
to be strongly localized. The greater the climate sensitivity, the larger the increase in precipitation
over the Great Plains. On the other hand, the implementation of a stabilization policy lead to
decreases in the magnitude of precipitation changes over the entire US. Finally, Figure 4 shows
the difference in the patterns of precipitation changes for the IGSM-pattern scaling. CCSM3.0
simulates decreases in precipitation over California, Arizona, most of New Mexico and the
8
Figure 4. Changes in precipitation (in mm/day) in 2100 (2091–2110 mean) relative to present day
(1991–2010 mean) for a) the IGSM-CAM simulations with different initial conditions under the
REF CS3.0 scenario, b) the IGSM-CAM ensemble mean with different climate sensitivities under the
REF scenario, c) the IGSM-CAM ensemble mean with different emissions scenarios under the CS3.0
scenario and d) the IGSM-pattern scaling with different climate models under the REF CS3.0 scenario.
western part of Nevada, and increases everywhere else. This pattern bears some resemblance with
the pattern of precipitation of the IGSM-CAM, except that the latter extends more along the north
of the Pacific Coast. BCCR BCM2.0 displays increases in precipitation over the entire US, except
for the southernmost part of Texas and Florida. MIROC3.2 MEDRES presents drying over the
Southern and Southwestern US and moistening elsewhere. Finally, the multi-model mean display
decreases in precipitation over the Western Texas, most of New Mexico and parts of Colorado,
Kansas and Arizona.
Figure 5 shows maps of the mean spread of temperature and precipitation changes in 2100
relative to present day for each source of uncertainty considered in this study (policy, climate
sensitivity, initial condition and model). The mean spread is defined as the standard deviation
across a source of uncertainty averaged over the other sources of uncertainty. Figure 5 reveals that
for temperature changes the mean spread displays little spatial heterogeneity. The largest source
of uncertainty is the choice of policy, with a mean spread between 2.0 and 3.0◦ C over most of the
US. The spread from the climate sensitivity is also substantial, with values between 1.0 and
9
Figure 5. Maps of the spread of changes in a) temperature (in ◦ C) and b) precipitation (in mm/day) in 2100
(2091–2110 mean) relative to present day (1991–2010 mean) for each source of uncertainty (policy,
climate sensitivity, initial condition and model pattern). The spread is defined as the standard deviation
across a source of uncertainty averaged over the other sources of uncertainty.
1.7◦ C. The impact of the choice of the model on temperature changes is much smaller, with a
mean spread reaching 0.8◦ C in only a few areas. Finally, the spread from initial conditions is
small, less than 0.5◦ C over the entire US. For precipitation changes, the spread of each source of
uncertainty is more heterogeneous. A particular feature is the small spread per the Southwest
region covering Southern California, Arizona, Utah and southern Nevada in all sources of
uncertainty, indicating that this region shows the least amount of uncertainty in precipitation
changes. The choice of policy and of model are the largest contributors of uncertainty in
precipitation changes, with a mean spread larger than 0.2 mm/day in large parts the Unites States.
Meanwhile, the mean spread associated with climate sensitivity lies between 0.08 and
0.18 mm/day tousled of the Southwestern US. The impact of initial conditions appears limited in
most regions except over the northern Pacific Coast, where the mean spread is larger than for the
climate sensitivity, and in the Southern US.
Finally, Figure 6 shows the same analysis as done for Figure 5 based on the US mean surface
air temperature and precipitation changes in 2025 (2016–2035 mean), 2050 (2041–2060 mean),
2075 ( 2066–2080 mean) and 2100 (2091–2110) mean relative to present day (1991–2010 mean).
It reveals that the relative contribution from each source of uncertainty changes over time and that
uncertainty sources contribute differently to temperature and precipitation changes. For changes
in temperature, the initial conditions are the largest source of uncertainty in 2025 but the spread
from initial conditions remains constant in time. As a result, its relative contribution decreases in
time. By 2050, the impact of policy and climate sensitivity are larger than that of the initial
conditions and their relative contribution continues to increase as the century advances. By 2100,
10
a) SPREAD OF U.S. MEAN TEMPERATURE CHANGE FROM 1981-2010
b) SPREAD OF U.S. MEAN PRECIPITATION CHANGE FROM 1981-2010
0.14
2.4
°C
1.6
1.2
Policy
0.12
Climate Sensivity
0.1
Initial Conditions
Models
mm/day
2
0.8
0.06
0.04
0.4
0
0.08
0.02
2025
2050
2075
0
2100
2025
2050
2075
2100
Figure 6. Spread of changes in a) US mean temperature (in ◦ C) and b) US mean precipitation (in mm/day)
in 2025 (2016–2035 mean), 2050 (2041–2060 mean), 2075 (2066–2085 mean) and 2100 (2091–2110
mean) relative to present day (1991–2010 mean) for each source of uncertainty (policy, climate
sensitivity, initial condition and model pattern). The spread is defined as the standard deviation across
a source of uncertainty averaged over the other sources of uncertainty.
the spread in temperature changes caused by differences in policy is more than twice as large as
the spread associated with the climate sensitivity. Meanwhile, the spread of temperature changes
associated with the choice of model is the smallest spread for every period analyzed. The impact
of the different sources of uncertainty on changes in precipitation is quite different from the
impact on temperature changes. In particular, the structural uncertainty associated with different
models is the largest source of uncertainty until 2100, when it is surpassed by policy. In addition,
the contribution of the initial conditions is on par with the contribution of the climate sensitivity.
The initial conditions are until 2075. Figure 6 underlines the complexity of the uncertainty in
projections of both regional surface temperature and precipitation. It also demonstrates the
importance of considering multiple sources of uncertainty in future regional climate projections.
4. SUMMARY AND CONCLUSION
As part of a multi-model project to achieve consistent evaluation of climate change impacts in
the US (Waldhoff et al., 2013), we use a series of 12 core simulations with the MIT IGSM with
three different emissions scenarios and four values of climate sensitivity (Paltsev et al., 2013). We
obtain regional climate change over the US using a two-pronged approach. On the one hand, we
use the MIT IGSM-CAM framework, which links the IGSM to the CAM model. On the other
hand, we apply a pattern scaling method to extend the latitudinal projections of the IGSM 2-D
zonal-mean atmosphere by applying longitudinally resolved patterns from IPCC AR4 climate
models. The IGSM-CAM is run for each of the 12 core simulations with five different initial
conditions to account for uncertainty in natural variability. The IGSM-pattern scaling method is
applied to three IPCC AR4 models and the multi-model mean based on 17 IPCC AR4 models.
The three models chosen are the NCAR CCSM3.0, which shares the same atmospheric model as
the IGSM-CAM; BCCR BCM2.0, which projects the largest increases in precipitation over the
11
US; and MIROC3.2 medres, which predicts the least amount of precipitation increases over the
US. This new framework for modeling uncertainty in regional climate change covers four
dimensions of uncertainty: projected emissions (different climate policies); global climate
response (different values of climate sensitivity and net aerosol forcing); natural variability
(different initial conditions); and structural uncertainty (different climate models). Altogether,
these simulations provide an efficient matrix of future climate projections to study climate
impacts under uncertainty.
The simulations display a large range of US mean temperature and precipitation changes, and
different regional patterns of change. In addition, the two different methods have very different
treatments of interannual variability. The IGSM-CAM physically simulates changes in both mean
climate and extreme events (Monier and Gao, 2013), but relies on one particular model. The
pattern scaling approach allows the spatial patterns of regional climate change of different climate
models to be considered, but significantly underestimates year-to-year variability and cannot
simulate extreme events or their potential changes under climate change. Together, these two
methods provide complementary skills and an efficient framework to investigate uncertainty in
future projections of regional climate change. The limitations of each methodology should be
carefully accounted for when using this framework to drive impact models. In particular,
researchers using climate simulations to drive impact models should always use individual model
simulations and not ensemble mean simulations in order to account for natural variability. That is
because natural variability is a driver for extreme climate and weather events, which can dominate
impacts, and would not be accounted for in ensemble mean simulations.
Finally, an analysis of the contribution of the four different sources of uncertainty reveals that
choice of policy and value of the climate sensitivity have the largest impact on surface air
temperature changes (choice of policy being the dominant contributor), while the contributions
from natural variability and structural uncertainty are small. On the other hand, the contributions
of the four sources of uncertainty are more equal for changes in US mean precipitation but show
large spatial heterogeneity. The impact of the initial conditions on precipitation changes is
substantial, in particular until 2050, on par with the impact of the climate sensitivity. The
structural uncertainty is the largest source of uncertainty until 2075. After that, the choice of
policy dominates. In light of these new results, it appears clear that the largest source of
uncertainty in future projections of climate change over the US is also the only source that society
has a control over: the emissions scenario. This should reflect the need to seriously consider
implementing a global climate policy aimed at stabilizing greenhouse gases concentrations in the
atmosphere.
It should be noted that the contribution of each source of uncertainty depends strongly on the
particular samples and choices made in this study. The implementation of only moderate policies
or the choice of only low values of climate sensitivity would certainly decrease the estimates of
their contribution to the overall changes. Nonetheless, this analysis demonstrates the relevance of
modeling each source of uncertainty. It further demonstrates the need of new and more complete
frameworks for modeling uncertainty in regional climate change.
12
Acknowledgements
This work was partially funded by the US Environmental Protection Agency under Cooperative
Agreement #XA-83600001. The Joint Program on the Science and Policy of Global Change is
funded by a number of federal agencies and a consortium of 40 industrial and foundation
sponsors. For a complete list of sponsors, see: http://globalchange.mit.edu. This research used the
Evergreen computing cluster at the Pacific Northwest National Laboratory. Evergreen is
supported by the Office of Science of the US Department of Energy under Contract No.
DE-AC05-76RL01830. The 20th Century Reanalysis V2 data was provided by the
NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at
http://www.esrl.noaa.gov/psd/.
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16
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FOR THE COMPLETE LIST OF JOINT PROGRAM REPORTS:
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